Forward-Backward smoothing for hidden markov models of point pattern data
MetadataShow full item record
Funding and Sponsorship
© 2017 IEEE. This paper considers a discrete-time sequential latent model for point pattern data, specifically a hidden Markov model (HMM) where each observation is an instantiation of a random finite set (RFS). This so-called RFS-HMM is worthy of investigation since point pattern data are ubiquitous in artificial intelligence and data science. We address the three basic problems typically encountered in such a sequential latent model, namely likelihood computation, hidden state inference, and parameter estimation. Moreover, we develop algorithms for solving these problems including forward-backward smoothing for likelihood computation and hidden state inference, and expectation-maximisation for parameter estimation. Simulation studies are used to demonstrate key properties of RFS-HMM, whilst real data in the domain of human dynamics are used to demonstrate its applicability.
Showing items related by title, author, creator and subject.
Duong, Thi V. T. (2008)Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, ...
Saptoro, Agus (2010)ANN technology exploded into the world of process modelling and control in the late 1980’s. The technology shows great promise and is seen as a technology that could provide models for most systems without the need to ...
Duong, Thi; Phung, Dinh; Bui, Hung H.; Venkatesh, Svetha (2009)A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit ...